Machine Learning Engineer

Machine Learning Engineer

This position is on site in Abu Dhabi.

I'm hiring for a Machine Learning Engineer for our multi national client that is building an AI COE in Abu Dhabi. They are working on moving from experimentation to scaled deployment across the group.

In this position you will be working on accelerating the deployment of AI and advanced analytics across core functions, moving from pilot initiatives to scaled, enterprise wide implementation.

You will be working on reshaping the way the group designs and delivers its services working with teams to turn emerging AI capabilities into practical solutions.

In this position you will design and develop scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision-making processes.

Key Responsibilities

• Work with large and complex data sets to solve challenging business problems.

• Efficient training and deployment of standard ML, NN, and Agentic models.

• Develop, train, and optimize machine learning models using state-of-the-art algorithms and

frameworks.

• Build production grade end-to-end ML pipelines, including data ingestion, transformation,

model training, validation, and deployment.

• Automate workflows for model training, testing, and deployment using CI/CD pipelines and

MLOps tools.

• Collaborate with cross-functional teams to integrate models into applications and deliver

end-to-end solutions.

• Finetune SLMs/LLMs and build complex AI architectures.

Qualifications / Experience

• (3-7) years of experience in building production grade, scalable AI systems.

• Expert in supervised/unsupervised learning, deep learning, NLP, computer vision, or

generative AI (e.g., LLMs).

• Strong ML system architecture skills.

• Understanding of model serving, API development (FastAPI, Flask), and optimizing model

performance for real-time or batch inference.

• General Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools like MLflow/Kubeflow

for model lifecycle management (MLOps).

• Comfortable with deploying models on AWS or Azure.

• Minimum Educational qualifications: Bachelor's degree in Computer Science, Engineering,

or related field required.

• Preferred Education qualifications: Master or PhD in Computer Science or a related field